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Argumentation Mining: State of the Art and Emerging Trends

Published:30 March 2016Publication History
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Abstract

Argumentation mining aims at automatically extracting structured arguments from unstructured textual documents. It has recently become a hot topic also due to its potential in processing information originating from the Web, and in particular from social media, in innovative ways. Recent advances in machine learning methods promise to enable breakthrough applications to social and economic sciences, policy making, and information technology: something that only a few years ago was unthinkable. In this survey article, we introduce argumentation models and methods, review existing systems and applications, and discuss challenges and perspectives of this exciting new research area.

References

  1. Palakorn Achananuparp, Xiaohua Hu, and Xiajiong Shen. 2008. The evaluation of sentence similarity measures. In Data Warehousing and Knowledge Discovery. Springer, 305--316.Google ScholarGoogle Scholar
  2. Ehud Aharoni, Anatoly Polnarov, Tamar Lavee, Daniel Hershcovich, Ran Levy, Ruty Rinott, Dan Gutfreund, and Noam Slonim. 2014. A benchmark dataset for automatic detection of claims and evidence in the context of controversial topics. In Proceedings of the 1st Workshop on Argumentation Mining. Association for Computational Linguistics, 64--68. http://acl2014.org/acl2014/W14-21/pdf/W14-2109.pdf.Google ScholarGoogle ScholarCross RefCross Ref
  3. Kevin D. Ashley and Vern R. Walker. 2013. Toward constructing evidence-based legal arguments using legal decision documents and machine learning. In ICAIL 2012, Enrico Francesconi and Bart Verheij (Eds.). ACM, 176--180.Google ScholarGoogle Scholar
  4. Bal Krishna Bal and Patrick Saint-Dizier. 2010. Towards building annotated resources for analyzing opinions and argumentation in news editorials. In Proceedings of the International Conference on Language Resources and Evaluation (LREC’10), Nicoletta Calzolari, Khalid Choukri, Bente Maegaard, Joseph Mariani, Jan Odijk, Stelios Piperidis, Mike Rosner, and Daniel Tapias (Eds.). European Language Resources Association.Google ScholarGoogle Scholar
  5. Pietro Baroni, Marco Romano, Francesca Toni, Marco Aurisicchio, and Giorgio Bertanza. 2015. Automatic evaluation of design alternatives with quantitative argumentation. Argument and Computation 6, 1 (2015), 24--49.Google ScholarGoogle ScholarCross RefCross Ref
  6. Trevor J. M. Bench-Capon and Paul E. Dunne. 2007. Argumentation in artificial intelligence. Artificial Intelligence 171, 10--15 (2007), 619--641.Google ScholarGoogle ScholarDigital LibraryDigital Library
  7. Jamal Bentahar, Bernard Moulin, and Micheline Bélanger. 2010. A taxonomy of argumentation models used for knowledge representation. Artificial Intelligence Review 33, 3 (2010), 211--259.Google ScholarGoogle ScholarDigital LibraryDigital Library
  8. Philippe Besnard, Alejandro Javier García, Anthony Hunter, Sanjay Modgil, Henry Prakken, Guillermo Ricardo Simari, and Francesca Toni. 2014. Introduction to structured argumentation. Argument and Computation 5, 1 (2014), 1--4.Google ScholarGoogle ScholarCross RefCross Ref
  9. Floris Bex, John Lawrence, Mark Snaith, and Chris Reed. 2013. Implementing the argument web. Communications of the ACM 56, 10 (Oct. 2013), 66--73.Google ScholarGoogle ScholarDigital LibraryDigital Library
  10. Or Biran and Owen Rambow. 2011. Identifying justifications in written dialogs by classifying text as argumentative. International Journal of Semantic Computing 5, 4 (2011), 363--381.Google ScholarGoogle ScholarCross RefCross Ref
  11. Christopher M. Bishop. 2006. Pattern Recognition and Machine Learning. Springer.Google ScholarGoogle ScholarDigital LibraryDigital Library
  12. Filip Boltuzic and Jan Snajder. 2014. Back up your stance: Recognizing arguments in online discussions. In Proceedings of the 1st Workshop on Argumentation Mining. Association for Computational Linguistics, 49--58.Google ScholarGoogle ScholarCross RefCross Ref
  13. Katarzyna Budzynska, Mathilde Janier, Juyeon Kang, Chris Reed, Patrick Saint-Dizier, Manfred Stede, and Olena Yaskorska. 2014. Towards argument mining from dialogue. In Proceedings of Computational Models of Argument (COMMA’14) (Frontiers in Artificial Intelligence and Applications), Simon Parsons, Nir Oren, Chris Reed, and Federico Cerutti (Eds.), Vol. 266. IOS Press, 185--196.Google ScholarGoogle Scholar
  14. Katarzyna Budzynska and Chris Reed. 2011. Whence Inference? Technical Report. University of Dundee.Google ScholarGoogle Scholar
  15. Elena Cabrio and Serena Villata. 2012a. Combining textual entailment and argumentation theory for supporting online debates interactions. In Proceedings of the 50th Annual Meeting of the Association for Computational Linguistics (ACL’12). Association for Computational Linguistics, 208--212.Google ScholarGoogle Scholar
  16. Elena Cabrio and Serena Villata. 2012b. Natural language arguments: A combined approach. In ECAI 2012—20th European Conference on Artificial Intelligence. Including Prestigious Applications of Artificial Intelligence (PAIS-2012) System Demonstrations Track, Luc De Raedt, Christian Bessière, Didier Dubois, Patrick Doherty, Paolo Frasconi, Fredrik Heintz, and Peter J. F. Lucas (Eds.), Vol. 242. IOS Press, 205--210.Google ScholarGoogle Scholar
  17. Elena Cabrio and Serena Villata. 2013. A natural language bipolar argumentation approach to support users in online debate interactions. Argument and Computation 4, 3 (2013), 209--230.Google ScholarGoogle ScholarCross RefCross Ref
  18. Elena Cabrio and Serena Villata. 2014. NoDE: A benchmark of natural language arguments. In Proceedings of COMMA 2014 (Frontiers in Artificial Intelligence and Applications), Simon Parsons, Nir Oren, Chris Reed, and Federico Cerutti (Eds.), Vol. 266. IOS Press, 449--450.Google ScholarGoogle Scholar
  19. Lynn Carlson, Daniel Marcu, and Mary Ellen Okurowski. 2002. RST Discourse Treebank. Technical Report LDC2002T07. Linguistic Data Consortium, Philadelphia. Web Download.Google ScholarGoogle Scholar
  20. Freddy Y. Y. Choi, Peter Wiemer-Hastings, and Johanna Moore. 2001. Latent semantic analysis for text segmentation. In Proceedings of EMNLP. Citeseer.Google ScholarGoogle Scholar
  21. Jim Crosswhite, John Fox, Chris Reed, Theodore Scaltsas, and Simone Stumpf. 2004. Computational models of rhetorical argument. In Argumentation Machines (Argumentation Library), Chris Reed and Timothy J. Norman (Eds.), Vol. 9. Springer, Netherlands, 175--209.Google ScholarGoogle Scholar
  22. Aron Culotta, Andrew McCallum, and Jonathan Betz. 2006. Integrating probabilistic extraction models and data mining to discover relations and patterns in text. In Proceedings of the Main Conference on Human Language Technology Conference of the North American Chapter of the Association of Computational Linguistics. Association for Computational Linguistics, 296--303.Google ScholarGoogle Scholar
  23. Jia Deng, Jonathan Krause, and Fei-Fei Li. 2013. Fine-grained crowdsourcing for fine-grained recognition. In 2013 IEEE Conference on Computer Vision and Pattern Recognition. IEEE, 580--587.Google ScholarGoogle ScholarDigital LibraryDigital Library
  24. Phan Minh Dung. 1995. On the acceptability of arguments and its fundamental role in nonmonotonic reasoning, logic programming and n-person games. Artificial Intelligence 77, 2 (1995), 321--358.Google ScholarGoogle ScholarDigital LibraryDigital Library
  25. David Easley and Jon Kleinberg. 2010. Networks, Crowds, and Markets. Reasoning About a Highly Connected World. Cambridge University Press.Google ScholarGoogle Scholar
  26. Judith Eckle-Kohler, Roland Kluge, and Iryna Gurevych. 2015. On the role of discourse markers for discriminating claims and premises in argumentative discourse. In Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, 2236--2242.Google ScholarGoogle ScholarCross RefCross Ref
  27. Andrea Esuli and Fabrizio Sebastiani. 2006. Determining term subjectivity and term orientation for opinion mining. In EACL, Vol. 6. 2006.Google ScholarGoogle Scholar
  28. James Fan, Aditya Kalyanpur, David C. Gondek, and D. A. Ferrucci. 2012. Automatic knowledge extraction from documents. IBM Journal of Research and Development 56, 3.4 (May 2012), 5:1--5:10.Google ScholarGoogle ScholarDigital LibraryDigital Library
  29. Vanessa Wei Feng and Graeme Hirst. 2011. Classifying arguments by scheme. In Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies-Volume 1. Association for Computational Linguistics, 987--996.Google ScholarGoogle ScholarDigital LibraryDigital Library
  30. James B. Freeman. 1991. Dialectics and the Macrostructure of Arguments: A Theory of Argument Structure. Vol. 10. Walter de Gruyter.Google ScholarGoogle Scholar
  31. Simone Gabbriellini and Paolo Torroni. 2014. A new framework for ABMs based on argumentative reasoning. In Advances in Social Simulation—Proceedings of the 9th Conference of the European Social Simulation Association (ESSA’13) (Advances in Intelligent Systems and Computing), Bogumil Kaminski and Grzegorz Koloch (Eds.), Vol. 229. Springer, 25--36.Google ScholarGoogle Scholar
  32. Simone Gabbriellini and Paolo Torroni. 2015. Microdebates: Structuring debates without a structuring tool. AI Communications.Google ScholarGoogle Scholar
  33. Lise Getoor. 2005. Tutorial on statistical relational learning. In ILP, Stefan Kramer and Bernhard Pfahringer (Eds.), Lecture Notes in Computer Science, Vol. 3625. Springer, 415.Google ScholarGoogle Scholar
  34. Lise Getoor and Christopher P. Diehl. 2005. Link mining: A survey. ACM SIGKDD Explorations Newsletter 7, 2 (2005), 3--12.Google ScholarGoogle ScholarDigital LibraryDigital Library
  35. Theodosis Goudas, Christos Louizos, Georgios Petasis, and Vangelis Karkaletsis. 2014. Argument extraction from news, blogs, and social media. In Artificial Intelligence: Methods and Applications, Aristidis Likas, Konstantinos Blekas, and Dimitris Kalles (Eds.). Lecture Notes in Computer Science, Vol. 8445. Springer International Publishing, 287--299.Google ScholarGoogle Scholar
  36. Nancy Green. 2014. Towards creation of a corpus for argumentation mining the biomedical genetics research literature. In Proceedings of the 1st Workshop on Argumentation Mining. Association for Computational Linguistics, 11--18.Google ScholarGoogle ScholarCross RefCross Ref
  37. Kathrin Grosse, María P. González, Carlos Iván Chesñevar, and Ana Gabriela Maguitman. 2015. Integrating argumentation and sentiment analysis for mining opinions from Twitter. AI Communications 28, 3 (2015), 387--401.Google ScholarGoogle ScholarCross RefCross Ref
  38. Adrien Guille, Hakim Hacid, Cécile Favre, and Djamel A. Zighed. 2013. Information diffusion in online social networks: A survey. ACM SIGMOD Record 42, 2 (2013), 17--28.Google ScholarGoogle ScholarDigital LibraryDigital Library
  39. Ivan Habernal, Judith Eckle-Kohler, and Iryna Gurevych. 2014. Argumentation mining on the Web from information seeking perspective. In Proceedings of the Workshop on Frontiers and Connections between Argumentation Theory and Natural Language Processing (CEUR Workshop Proceedings), Elena Cabrio, Serena Villata, and Adam Wyner (Eds.), Vol. 1341. CEUR-WS.org. http://ceur-ws.org/Vol-1341/paper4.pdf.Google ScholarGoogle Scholar
  40. Alexander Hogenboom, Frederik Hogenboom, Uzay Kaymak, Paul Wouters, and Franciska De Jong. 2010. Mining economic sentiment using argumentation structures. In Advances in Conceptual Modeling—Applications and Challenges. Springer, 200--209.Google ScholarGoogle Scholar
  41. Hospice Houngbo and Robert Mercer. 2014. An automated method to build a corpus of rhetorically-classified sentences in biomedical texts. In Proceedings of the 1st Workshop on Argumentation Mining. Association for Computational Linguistics, 19--23. http://acl2014.org/acl2014/W14-21/pdf/W14-2103.pdfGoogle ScholarGoogle ScholarCross RefCross Ref
  42. Muhammad Imran, Shady Elbassuoni, Carlos Castillo, Fernando Diaz, and Patrick Meier. 2013. Practical extraction of disaster-relevant information from social media. In Proceedings of the 22nd International Conference on World Wide Web Companion (WWW’13 Companion). International World Wide Web Conferences Steering Committee, Republic and Canton of Geneva, Switzerland, 1021--1024.Google ScholarGoogle Scholar
  43. Noriaki Kawamae. 2011. Predicting future reviews: Sentiment analysis models for collaborative filtering. In Proceedings of the 4th ACM International Conference on Web Search and Data Mining. ACM, 605--614.Google ScholarGoogle ScholarDigital LibraryDigital Library
  44. Yoon Kim. 2014. Convolutional neural networks for sentence classification. In Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP’14), A meeting of SIGDAT, a Special Interest Group of the ACL, Alessandro Moschitti, Bo Pang, and Walter Daelemans (Eds.). ACL, 1746--1751.Google ScholarGoogle Scholar
  45. Christian Kirschner, Judith Eckle-Kohler, and Iryna Gurevych. 2015. Linking the thoughts: Analysis of argumentation structures in scientific publications. In Proceedings of the 2nd Workshop on Argumentation Mining. Association for Computational Linguistics, 1--11.Google ScholarGoogle ScholarCross RefCross Ref
  46. Werner Kunz and Horst W. J. Rittel. 1970. Issues as Elements of Information Systems. Institute of Urban and Regional Development, University of California, Vol. 131. Berkeley, California.Google ScholarGoogle Scholar
  47. John Lawrence, Chris Reed, Colin Allen, Simon McAlister, and Andrew Ravenscroft. 2014. Mining arguments from 19th century philosophical texts using topic based modelling. In Proceedings of the 1st Workshop on Argumentation Mining. Association for Computational Linguistics, 79--87.Google ScholarGoogle ScholarCross RefCross Ref
  48. Yann LeCun, Yoshua Bengio, and Geoffrey Hinton. 2015. Deep learning. Nature 531 (2015), 436--444.Google ScholarGoogle ScholarCross RefCross Ref
  49. Cane W. K. Leung, Stephen C. F. Chan, and Fu-lai Chung. 2006. Integrating collaborative filtering and sentiment analysis: A rating inference approach. In Proceedings of the ECAI 2006 Workshop on Recommender Systems. Citeseer, 62--66.Google ScholarGoogle Scholar
  50. Ran Levy, Yonatan Bilu, Daniel Hershcovich, Ehud Aharoni, and Noam Slonim. 2014. Context dependent claim detection. In COLING 2014, Jan Hajic and Junichi Tsujii (Eds.). ACL, 1489--1500. http://www.aclweb.org/anthology/C14-1141.Google ScholarGoogle Scholar
  51. Ziheng Lin, Min-Yen Kan, and Hwee Tou Ng. 2009. Recognizing implicit discourse relations in the penn discourse treebank. In Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing: Volume 1. Association for Computational Linguistics, 343--351.Google ScholarGoogle ScholarDigital LibraryDigital Library
  52. Eric Lindahl, Stephen O’Hara, and Qiuming Zhu. 2007. A multi-agent system of evidential reasoning for intelligence analyses. In AAMAS, Edmund H. Durfee, Makoto Yokoo, Michael N. Huhns, and Onn Shehory (Eds.). IFAAMAS, 279.Google ScholarGoogle Scholar
  53. Marco Lippi and Paolo Torroni. 2015. Context-independent claim detection for argument mining. In Proceedings of the 24th International Joint Conference on Artificial Intelligence (IJCAI’15), Qiang Yang and Michael Wooldridge (Eds.). AAAI Press, 185--191.Google ScholarGoogle ScholarDigital LibraryDigital Library
  54. Christopher D. Manning and Hinrich Schütze. 2001. Foundations of Statistical Natural Language Processing. MIT Press.Google ScholarGoogle ScholarDigital LibraryDigital Library
  55. Michael Mäs and Andreas Flache. 2013. Differentiation without distancing. Explaining bi-polarization of opinions without negative influence. PLoS ONE 8, 11 (Nov. 2013), e74516.Google ScholarGoogle ScholarCross RefCross Ref
  56. Hugo Mercier and Dan Sperber. 2011. Why do humans reason? Arguments for an argumentative theory. Behavioral and Brain Sciences 34, 2 (April 2011), 57--74.Google ScholarGoogle ScholarCross RefCross Ref
  57. Tomas Mikolov, Kai Chen, Greg Corrado, and Jeffrey Dean. 2013. Efficient estimation of word representations in vector space. CoRR abs/1301.3781 (2013). http://arxiv.org/abs/1301.3781Google ScholarGoogle Scholar
  58. Michela Milano, Barry O’Sullivan, and Marco Gavanelli. 2014. Sustainable policy making: A strategic challenge for artificial intelligence. AI Magazine 35, 3 (2014), 22--35.Google ScholarGoogle ScholarCross RefCross Ref
  59. Raquel Mochales Palau and Aagje Ieven. 2009. Creating an argumentation corpus: Do theories apply to real arguments? A case study on the legal argumentation of the ECHR. In Proceedings of the 12th International Conference on Artificial Intelligence and Law (ICAIL’09). ACM, 21--30.Google ScholarGoogle Scholar
  60. Raquel Mochales Palau and Marie-Francine Moens. 2011. Argumentation mining. Artificial Intelligence and Law 19, 1 (2011), 1--22.Google ScholarGoogle ScholarDigital LibraryDigital Library
  61. Sanjay Modgil, Francesca Toni, Floris Bex, Ivan Bratko, Carlos I. Chesñevar, Wolfgang Dvorák, Marcelo A. Falappa, Xiuyi Fan, Sarah A. Gaggl, Alejandro J. García, María P. González, Thomas F. Gordon, João Leite, Martin Molina, Chris Reed, Guillermo R. Simari, Stefan Szeider, Paolo Torroni, and Stefan Woltran. 2013. The added value of argumentation. In Agreement Technologies. Springer-Verlag, 357--404.Google ScholarGoogle Scholar
  62. Marie-Francine Moens. 2014. Argumentation mining: Where are we now, where do we want to be and how do we get there? In Post-Proceedings of the Forum for Information Retrieval Evaluation (FIRE’13).Google ScholarGoogle Scholar
  63. Alessandro Moschitti. 2006. Efficient convolution kernels for dependency and constituent syntactic trees. In Machine Learning: ECML 2006, Johannes Frnkranz, Tobias Scheffer, and Myra Spiliopoulou (Eds.). Lecture Notes in Computer Science, Vol. 4212. Springer, Berlin, 318--329.Google ScholarGoogle Scholar
  64. David Nadeau and Satoshi Sekine. 2007. A survey of named entity recognition and classification. Lingvisticae Investigationes 30, 1 (2007), 3--26.Google ScholarGoogle ScholarCross RefCross Ref
  65. Susan E. Newman and Catherine C. Marshall. 1991. Pushing Toulmin Too Far: Learning From an Argument Representation Scheme. Technical Report. Xerox Palo Alto Research Center, Palo Alto, CA.Google ScholarGoogle Scholar
  66. Nam Nguyen and Yunsong Guo. 2007. Comparisons of sequence labeling algorithms and extensions. In Proceedings of the 24th International Conference on Machine Learning. ACM, 681--688.Google ScholarGoogle ScholarDigital LibraryDigital Library
  67. Stefanie Nowak and Stefan Rüger. 2010. How reliable are annotations via crowdsourcing: A study about inter-annotator agreement for multi-label image annotation. In Proceedings of the International Conference on Multimedia Information Retrieval. ACM, New York, NY, 557--566.Google ScholarGoogle ScholarDigital LibraryDigital Library
  68. Nathan Ong, Diane Litman, and Alexandra Brusilovsky. 2014. Ontology-based argument mining and automatic essay scoring. In Proceedings of the 1st Workshop on Argumentation Mining. Association for Computational Linguistics, 24--28.Google ScholarGoogle ScholarCross RefCross Ref
  69. Sebastian Padò, Gil Noh, Asher Stern, Rui Wang, and Robert Zanol. 2013. Design and realization of a modular architecture for textual entailment. Journal of Natural Language Engineering 1 (Dec. 2013), 1--34.Google ScholarGoogle Scholar
  70. Sinno Jialin Pan and Qiang Yang. 2010. A survey on transfer learning. IEEE Transactions on Knowledge and Data Engineering 22, 10 (Oct. 2010), 1345--1359.Google ScholarGoogle ScholarDigital LibraryDigital Library
  71. Bo Pang and Lillian Lee. 2008. Opinion mining and sentiment analysis. Foundations and Trends in Information Retrieval 2, 1--2 (Jan. 2008), 1--135.Google ScholarGoogle ScholarDigital LibraryDigital Library
  72. Joonsuk Park and Claire Cardie. 2014. Identifying appropriate support for propositions in online user comments. In Proceedings of the 1st Workshop on Argumentation Mining. Association for Computational Linguistics, 29--38. http://www.aclweb.org/anthology/W/W14/W14-2105.Google ScholarGoogle ScholarCross RefCross Ref
  73. Joonsuk Park, Arzoo Katiyar, and Bishan Yang. 2015. Conditional random fields for identifying appropriate types of support for propositions in online user comments. In Proceedings of the 2nd Workshop on Argumentation Mining. Association for Computational Linguistics.Google ScholarGoogle ScholarCross RefCross Ref
  74. Andreas Peldszus. 2014. Towards segment-based recognition of argumentation structure in short texts. In Proceedings of the 1st Workshop on Argumentation Mining. Association for Computational Linguistics, 88--97.Google ScholarGoogle ScholarCross RefCross Ref
  75. Andreas Peldszus and Manfred Stede. 2013. From argument diagrams to argumentation mining in texts: A survey. International Journal of Cognitive Informatics and Natural Intelligence 7, 1 (2013), 1--31.Google ScholarGoogle ScholarDigital LibraryDigital Library
  76. John L. Pollock. 1987. Defeasible reasoning. Cognitive Science 11, 4 (1987), 481--518.Google ScholarGoogle ScholarCross RefCross Ref
  77. Hoifung Poon and Pedro Domingos. 2007. Joint inference in information extraction. In Proceedings of the 22nd AAAI Conference on Artificial Intelligence. AAAI Press, 913--918.Google ScholarGoogle Scholar
  78. Hoifung Poon and Pedro Domingos. 2009. Unsupervised semantic parsing. In Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing: Volume 1(EMNLP’09). Association for Computational Linguistics, Stroudsburg, PA, 1--10. http://dl.acm.org/citation.cfm?id=1699510.1699512.Google ScholarGoogle ScholarCross RefCross Ref
  79. Paul Reisert, Junta Mizuno, Miwa Kanno, Naoaki Okazaki, and Kentaro Inui. 2014. A corpus study for identifying evidence on microblogs. In Proceedings of LAW VIII—The 8th Linguistic Annotation Workshop. Association for Computational Linguistics and Dublin City University, 70--74. http://aclweb.org/anthology/W14-4910.Google ScholarGoogle ScholarCross RefCross Ref
  80. Ruty Rinott, Lena Dankin, Carlos Alzate Perez, Mitesh M. Khapra, Ehud Aharoni, and Noam Slonim. 2015. Show me your evidence—An automatic method for context dependent evidence detection. In Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing (EMNLP’15), Lluís Màrquez, Chris Callison-Burch, Jian Su, Daniele Pighin, and Yuval Marton (Eds.). The Association for Computational Linguistics, 440--450.Google ScholarGoogle ScholarCross RefCross Ref
  81. Alan Ritter, Sam Clark, Mausam, and Oren Etzioni. 2011. Named entity recognition in tweets: An experimental study. In Proceedings of the Conference on Empirical Methods in Natural Language Processing (EMNLP’11). Association for Computational Linguistics, Stroudsburg, PA, 1524--1534. http://dl.acm.org/citation.cfm?id=2145432.2145595.Google ScholarGoogle ScholarDigital LibraryDigital Library
  82. Niall Rooney, Hui Wang, and Fiona Browne. 2012. Applying kernel methods to argumentation mining. In Proceedings of the 25th International Florida Artificial Intelligence Research Society Conference, G. Michael Youngblood and Philip M. McCarthy (Eds.). AAAI Press. http://www.aaai.org/ocs/index.php/FLAIRS/FLAIRS12/paper/view/4366.Google ScholarGoogle Scholar
  83. Sara Rosenthal and Kathleen McKeown. 2012. Detecting opinionated claims in online discussions. In 6th IEEE International Conference on Semantic Computing (ICSC’12). IEEE Computer Society, 30--37.Google ScholarGoogle ScholarDigital LibraryDigital Library
  84. Patrick Saint-Dizier. 2012. Processing natural language arguments with the<TextCoop>platform. Argument and Computation 3, 1 (2012), 49--82.Google ScholarGoogle ScholarCross RefCross Ref
  85. Christos Sardianos, Ioannis Manousos Katakis, Georgios Petasis, and Vangelis Karkaletsis. 2015. Argument extraction from news. In Proceedings of the 2nd Workshop on Argumentation Mining, Lecture Notes in Computer Science, Vol. 8445 (2015), 56--66.Google ScholarGoogle Scholar
  86. John Scott. 2012. Social Network Analysis. Sage.Google ScholarGoogle Scholar
  87. Fabrizio Sebastiani. 2002. Machine learning in automated text categorization. ACM Computing Surveys 34, 1 (2002), 1--47.Google ScholarGoogle ScholarDigital LibraryDigital Library
  88. Guillermo R. Simari and Ronald P. Loui. 1992. A mathematical treatment of defeasible reasoning and its implementation. Artificial Intelligence 53, 23 (1992), 125--157.Google ScholarGoogle ScholarDigital LibraryDigital Library
  89. Richard Socher, Alex Perelygin, Jean Y. Wu, Jason Chuang, Christopher D. Manning, Andrew Y. Ng, and Christopher Potts. 2013. Recursive deep models for semantic compositionality over a sentiment treebank. In Proceedings of the Conference on Empirical Methods in Natural Language Processing (EMNLP), Vol. 1631. Citeseer, 1642.Google ScholarGoogle Scholar
  90. Christian Stab and Iryna Gurevych. 2014a. Annotating argument components and relations in persuasive essays. In COLING 2014, Jan Hajic and Junichi Tsujii (Eds.). ACL, 1501--1510.Google ScholarGoogle Scholar
  91. Christian Stab and Iryna Gurevych. 2014b. Identifying argumentative discourse structures in persuasive essays. In EMNLP 2014, Alessandro Moschitti, Bo Pang, and Walter Daelemans (Eds.). ACL, 46--56.Google ScholarGoogle ScholarCross RefCross Ref
  92. Kai Sheng Tai, Richard Socher, and Christopher D. Manning. 2015. Improved semantic representations from tree-structured long short-term memory networks. CoRR abs/1503.00075 (2015).Google ScholarGoogle Scholar
  93. Simone Teufel. 1999. Argumentative Zoning. PhD thesis. University of Edinburgh.Google ScholarGoogle Scholar
  94. Stephen Edelston Toulmin. 1958. The Uses of Argument. Cambridge University Press.Google ScholarGoogle Scholar
  95. Mathias Verbeke, Paolo Frasconi, Vincent Van Asch, Roser Morante, Walter Daelemans, and Luc De Raedt. 2012. Kernel-based logical and relational learning with kLog for hedge cue detection. In Inductive Logic Programming. Springer, 347--357.Google ScholarGoogle Scholar
  96. Maria Paz Garcia Villalba and Patrick Saint-Dizier. 2012. Some facets of argument mining for opinion analysis. In COMMA 2012 (Frontiers in Artificial Intelligence and Applications), Bart Verheij, Stefan Szeider, and Stefan Woltran (Eds.), Vol. 245. IOS Press, 23--34.Google ScholarGoogle Scholar
  97. Douglas Walton. 2009. Argumentation theory: A very short introduction. In Argumentation in Artificial Intelligence, Guillermo Simari and Iyad Rahwan (Eds.). Springer, 1--22.Google ScholarGoogle Scholar
  98. Dell Zhang and Wee Sun Lee. 2003. Question classification using support vector machines. In Proceedings of the 26th Annual International ACM SIGIR Conference on Research and Development in Informaion Retrieval. ACM, 26--32.Google ScholarGoogle ScholarDigital LibraryDigital Library

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            cover image ACM Transactions on Internet Technology
            ACM Transactions on Internet Technology  Volume 16, Issue 2
            April 2016
            150 pages
            ISSN:1533-5399
            EISSN:1557-6051
            DOI:10.1145/2909066
            • Editor:
            • Munindar P. Singh
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            Publication History

            • Published: 30 March 2016
            • Accepted: 1 November 2015
            • Revised: 1 October 2015
            • Received: 1 June 2015
            Published in toit Volume 16, Issue 2

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